A multi-label waste detection model based on transfer learning
2022
Zhang, Qiang | Yang, Qifan | Zhang, Xujuan | Wei, Wei | Bao, Qiang | Su, Jinqi | Liu, Xueyan
Accurate and efficient treatment of domestic waste is an important part of urban management. Whether domestic waste can be classified effectively will affect the sustainable development of human society. Previous research on the problem of waste image classification has focused on single-category waste recognition, which falls short of meeting the needs of real waste classification scenarios. In this study, a YOLO-WASTE multi-label waste classification model based on transfer learning is constructed to realize the fast recognition and classification of multiple wastes. To speed up and optimize the learning efficiency of the model, a multi-label waste image dataset is also created, with each image including multiple wastes or multiple categories of waste. The experimental result shows that the mAP value of the YOLO-WASTE model is 92.23%, and the average time of detecting an image is 0.424 s, its classification performance is significantly better than other image classification algorithms. The proposed YOLO-WASTE model provides new insights into complex waste identification and has the potential to help advance efficient waste management for sustainable urban development.
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